What Is Natural Language Processing?

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Natural language processing (NLP) pertains to the branch of artificial intelligence that enables computers to understand, process, interpret, and generate text and spoken words of the human language.

 

How NLP Works

Human language is highly complex, irregular, and diverse. Dialects, accents, homophones, syntax, grammatical rules, abbreviations, misspellings, and slang are some aspects that make it challenging to create a program that understands the underlying meaning of text or voice data.

NLP works by using computational linguistics, the rule-based modeling of human language, and combines it with statistical, machine learning, and deep learning models. It breaks down raw language data into shorter elements through several NLP tasks to help computers process and understand the input.

Some of the underlying tasks are as follows:

  • Speech Recognition: Also called speech-to-text, it converts voice data into written text. It is essential in applications that respond to voice commands and spoken questions.
  • Text-to-Speech Conversion: The opposite of speech recognition, this transforms text data input into human language response.
  • Grammatical Tagging: Known as part-of-speech tagging, it identifies a particular word as part of a speech according to its use and context.
  • Named Entity Recognition: This task determines words or phrases as entities. For instance, “Washington” may be recognized as a place or a name.
  • Machine Translation: This automatically translates voice or text data to another language.
  • Sentiment Analysis: This attempts to identify the subjective qualities, such as mood, attitude, opinion, emotion, and sarcasm, within the text.

 

What Are NLP Use Cases?

There are countless practical applications for natural language processing. Below are a few examples: 

  • Spam Detection: Developers use NLP’s text classification to scan emails for common language indicators suggesting spam or scam. Bad grammar, misspellings, inappropriate urgency, and threatening language are typical features of junk email.
  • Chatbots and Virtual Agents: Chatbots use NLP capability to recognize contextual clues on typed text requests or queries to respond appropriately or as accurately as possible. On the other hand, digital assistants like Siri and Alexa use speech recognition.
  • Document Summarization: NLP’s text summarization technique allows applications to create summaries and synopses from large volumes of digital text. Some of the most efficient applications can also add context and conclusions because of semantic reasoning and natural language generation.
  • Social Media Analytics: Sentiment analysis via NLP lets businesses track attitudes and emotions about specific topics on social media channels and use gathered insights in their strategies.

 

NLP & Pachyderm

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